Filetype Excel XLSX | Posted on 10 Aug 2022 | 3 years ago
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159x Filetype XLSX File size 0.40 MB Source: people.duke.edu
Week PRICE 12PK PRICE_12PK_LN PRICE 18PK PRICE_18PK_LN PRICE 30PK PRICE_30PK_LN
1 19.98 2.995 14.10 2.646 15.19 2.721
2 19.98 2.995 18.65 2.926 15.19 2.721
3 19.98 2.995 18.65 2.926 13.87 2.630
4 19.98 2.995 18.65 2.926 12.83 2.552
The data consists of 52 weeks of cases-sold and price-per-case data for 3 carton sizes of beer (12-packs, 18-packs,
5 19.98 2.995 18.65 2.926 13.16 2.577
30-packs) at a small chain of supermarkets.
6 19.98 2.995 18.65 2.926 15.19 2.721
Six additional rows of hypothetical price data for 18-packs have been entered for purposes of forecasting from the
7 19.98 2.995 18.65 2.926 13.92 2.633
models. (Forecasts are automatically generated when the models are fitted.)
8 20.10 3.001 18.73 2.930 14.42 2.669
9 20.12 3.002 18.75 2.931 13.83 2.627
The variable transformation tool in RegressIt has been used to apply the natural log transformation to all of the
original sales and price variables. The names of the logged variables end in "_LN".
10 20.13 3.002 18.75 2.931 14.50 2.674
11 20.14 3.003 18.75 2.931 13.87 2.630
12 20.12 3.002 18.75 2.931 13.64 2.613
13 20.12 3.002 13.87 2.630 14.31 2.661
14 20.13 3.002 14.27 2.658 13.85 2.628
15 20.14 3.003 18.76 2.932 14.20 2.653
16 20.14 3.003 18.77 2.932 13.64 2.613
17 20.13 3.002 13.87 2.630 14.33 2.662
18 20.13 3.002 14.14 2.649 13.14 2.576
19 20.13 3.002 18.76 2.932 13.81 2.625
20 20.13 3.002 18.72 2.930 15.19 2.721
21 20.13 3.002 18.76 2.932 13.13 2.575
22 19.18 2.954 18.76 2.932 13.63 2.612
23 14.78 2.693 18.74 2.931 15.19 2.721
24 16.04 2.775 18.75 2.931 13.89 2.631
25 20.12 3.002 18.75 2.931 14.28 2.659
26 19.75 2.983 18.75 2.931 15.19 2.721
27 19.65 2.978 18.75 2.931 13.12 2.574
28 19.69 2.980 13.79 2.624 13.78 2.623
29 20.12 3.002 13.49 2.602 15.19 2.721
30 20.12 3.002 14.89 2.701 15.19 2.721
31 20.13 3.002 13.94 2.635 15.19 2.721
32 20.14 3.003 13.67 2.615 15.19 2.721
33 15.14 2.717 14.43 2.669 15.19 2.721
34 14.33 2.662 18.75 2.931 15.19 2.721
35 16.24 2.787 18.22 2.903 13.14 2.576
36 19.93 2.992 14.06 2.643 13.45 2.599
37 21.06 3.047 14.43 2.669 13.00 2.565
38 21.19 3.054 19.48 2.969 13.60 2.610
39 21.23 3.055 15.15 2.718 14.46 2.671
40 20.12 3.002 13.79 2.624 14.94 2.704
41 14.73 2.690 14.31 2.661 15.19 2.721
42 14.57 2.679 19.50 2.970 15.19 2.721
43 15.94 2.769 13.85 2.628 15.19 2.721
44 20.70 3.030 14.23 2.655 13.43 2.597
45 19.57 2.974 19.31 2.961 14.37 2.665
46 19.60 2.976 19.29 2.960 15.19 2.721
47 19.94 2.993 13.76 2.622 15.19 2.721
48 21.28 3.058 13.45 2.599 15.19 2.721
49 14.56 2.678 15.13 2.717 15.19 2.721
50 14.39 2.667 19.43 2.967 15.19 2.721
51 16.81 2.822 13.26 2.585 15.19 2.721
52 19.86 2.99 13.92 2.633 15.19 2.721
53 13.00 2.565
54 14.00 2.639
55 15.00 2.708
56 16.00 2.773
57 17.00 2.833
58 18.00 2.890
59 19.00 2.944
60 20.00 2.996
CASES 12PK CASES_12PK_LN CASES 18PK CASES_18PK_LN CASES 30PK CASES_30PK_LN
223.5 5.409 439 6.0845 55.00 4.007
215.0 5.371 98 4.5850 66.75 4.201
227.5 5.427 70 4.2485 242.00 5.489
244.5 5.499 52 3.9512 488.50 6.191
The data consists of 52 weeks of cases-sold and price-per-case data for 3 carton sizes of beer (12-packs, 18-packs,
30-packs) at a small chain of supermarkets.313.55.748 64 4.1589 308.75 5.733
279.0 5.631 72 4.2767 111.75 4.716
Six additional rows of hypothetical price data for 18-packs have been entered for purposes of forecasting from the
238.0 5.472 47 3.8501 252.50 5.531
models. (Forecasts are automatically generated when the models are fitted.)315.55.754854.4427221.25 5.399
217.0 5.380 59 4.0775 245.25 5.502
The variable transformation tool in RegressIt has been used to apply the natural log transformation to all of the
original sales and price variables. The names of the logged variables end in "_LN".209.55.345634.1431148.505.001
227.0 5.425 57 4.0431 229.75 5.437
216.5 5.378 54 3.9890 312.00 5.743
169.0 5.130 404 6.0014 96.75 4.572
178.0 5.182 380 5.9402 123.25 4.814
301.5 5.709 65 4.1744 200.50 5.301
266.5 5.585 40 3.6889 359.75 5.885
182.5 5.207 456 6.1225 113.50 4.732
159.0 5.069 176 5.1705 136.50 4.916
285.5 5.654 61 4.1109 225.50 5.418
360.0 5.886 91 4.5109 122.25 4.806
263.0 5.572 59 4.0775 443.75 6.095
443.5 6.095 83 4.4188 322.75 5.777
1101.5 7.004 41 3.7136 53.00 3.970
814.0 6.702 47 3.8501 140.75 4.947
365.0 5.900 84 4.4308 210.75 5.351
510.0 6.234 85 4.4427 110.50 4.705
580.5 6.364 116 4.7536 568.25 6.343
251.0 5.525 544 6.2989 115.50 4.749
237.0 5.468 890 6.7912 58.75 4.073
302.5 5.712 371 5.9162 77.25 4.347
229.5 5.436 557 6.3226 66.25 4.193
188.5 5.239 775 6.6529 50.00 3.912
795.5 6.679 236 5.4638 46.50 3.839
1556.5 7.350 43 3.7612 65.75 4.186
807.5 6.694 63 4.1431 252.75 5.532
243.0 5.493 469 6.1506 179.00 5.187
201.5 5.306 335 5.8141 226.25 5.422
294.0 5.684 75 4.3175 288.50 5.665
220.5 5.396 461 6.1334 114.25 4.738
255.5 5.543 817 6.7056 70.00 4.248
920.5 6.825 200 5.2983 47.75 3.866
730.0 6.593 32 3.4657 98.75 4.593
262.5 5.570 460 6.1312 77.00 4.344
209.5 5.345 751 6.6214 160.50 5.078
283.0 5.645 70 4.2485 143.50 4.966
262.5 5.570 80 4.3820 133.00 4.890
310.0 5.737 523 6.2596 68.75 4.230
278.5 5.629 741 6.6080 81.75 4.404
741.5 6.609 130 4.8675 56.25 4.030
1316.0 7.182 69 4.2341 68.75 4.230
449.0 6.107 493 6.2005 49.25 3.897
505.0 6.225 814 6.7020 76.50 4.337
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